1. Field of the Invention
This invention relates to an apparatus and a method for modeling and controlling an industrial process, and more particularly, to an apparatus and a method for adaptively modeling and controlling an industrial process.
2. Description of the Related Art
In industrial environments such as those in oil refineries, chemical plants and power plants, numerous processes need to be tightly controlled to meet the required specifications for the resulting products. The control of processes in the plant is provided by a process control apparatus which typically senses a number of input/output variables such as material compositions, feed rates, feedstock temperatures, and product formation rate. The process control apparatus then compares these variables against desired predetermined values. If unexpected differences exist, changes are made to the input variables to return the output variables to a predetermined desired range.
Traditionally, the control of a process is provided by a proportional-integral-derivative (PID) controller. PID controllers provide satisfactory control behavior for many single input/single output (SISO) systems whose dynamics change within a relatively small range. However, as each PID controller has only one input variable and one output variable, the PID controller lacks the ability to control a system with multivariable input and outputs. Although a number of PID controllers can be cascaded together in series or in parallel, the complexity of such an arrangement often limits the confidence of the user in the reliability and accuracy of the control system. Thus the adequacy of the process control may be adversely affected. Hence, PID controllers have difficulties controlling complex, non-linear systems such as chemical reactors, blast furnaces, distillation columns, and rolling mills.
Additionally, plant processes may be optimized to improve the plant throughput or the product quality, or both. The optimization of the manufacturing process typically is achieved by controlling variables that are not directly or instantaneously controllable. Historically, a human process expert can empirically derive an algorithm to optimize the indirectly controlled variable. However, as the number of process variables that influence indirectly controlled variables increases, the complexity of the optimization process rises exponentially. Since this condition quickly becomes unmanageable, process variables with minor influence in the final solution are ignored. Although each of these process variables exhibits a low influence when considered alone, the cumulative effect of the omissions can greatly reduce the process control model's accuracy and usability. Alternatively, the indirectly-controlled variables may be solved using numerical methods. However, as the numerical solution is computationally intensive, it may not be possible to perform the process control in real-time.
The increasing complexity of industrial processes, coupled with the need for real-time process control, is driving process control systems toward making experience-based judgments akin to human thinking in order to cope with unknown or unanticipated events affecting the optimization of the process. One control method based on expert system technology, called expert control or intelligent control, represents a step in the adaptive control of these complex industrial systems. Based on the knowledge base of the expert system, the expert system software can adjust the process control strategy after receiving inputs on changes in the system environment and control tasks. However, as the expert system depends heavily on a complete transfer of the human expert's knowledge and experience into an electronic database, it is difficult to produce an expert system capable of handling the dynamics of a complex system.
Recently, neural network based systems have been developed which provide powerful self-learning and adaptation capabilities to cope with uncertainties and changes in the system environment. Modelled after biological neural networks, engineered neural networks process training data and formulate a matrix of coefficients representative of the firing thresholds of biological neural networks. The -matrix of coefficients are derived by repetitively circulating data through the neural network in training sessions and adjusting the weights in the coefficient matrix until the outputs of the neural networks are within predetermined ranges of the expected outputs of the training data. Thus, after training, a generic neural network conforms to the particular task assigned to the neural network. This property is common to a large class of flexible functional form models known as non-parametric models, which includes neural networks, Fourier series, smoothing splines, and kernel estimators.
The neural network model is suitable for modeling complex chemical processes such as non-linear industrial processes due to its ability to approximate arbitrarily complex functions. Further, the data derived neural network model can be developed without a detailed knowledge of the underlying processes. Although the neural network has powerfull self-learning and adaptation capabilities to cope with uncertainties and changes in its environment, the lack of a process-based internal structure can be a liability for the neural network. For instance, when training data is limited and noisy, the network outputs may not conform to known process constraints. For example, certain process variables are known to increase monotonically as they approach their respective asymptotic limits. Both the monotonicity and the asymptotic limits are factors that should be enforced on a neural network when modeling these variables. However, the lack of training data may prevent a neural network from capturing either. Thus, neural network models have been criticized on the basis that 1) they are empirical; 2) they possess no physical basis, and 3) they produce results that are possibly inconsistent with prior experience.
Insufficient data may thus hamper the accuracy of a neural network due to the network's pure reliance on training data when inducing process behavior. Qualitative knowledge of a function to be modeled, however, may be used to overcome the sparsity of training data. A number of approaches have been utilized to exploit prior known information and to reduce the dependence on the training data alone. One approach deploys a semi-parametric design which applies a parametric model in tandem with the neural network. As described by S. J. Qin and T. J. McAvoy in "Nonlinear PLS Modeling Using Neural Networks", Computers Chem. Engng., Vol. 16, No. 4, pp. 379-391 (1992), a parametric model has a fixed structure derived from a first principle which can be existing empirical correlations or known mathematical transformations. The neural network may be used in a series approach to estimate intermediate variables to be used in the parametric model.
Alternatively, a parallel semi-parametric approach can be deployed where the outputs of the neural network and the parametric model are combined to determine the total model output. The model serves as an idealized estimator of the process or a best guess at the process model. The neural network is trained on the residual between the data and the parametric model to compensate for uncertainties that arise from the inherent process complexity.
Although the semi-parametric model provides a more accurate model than either the parametric model or the neural network model alone, it requires prior knowledge, as embodied in the first principle in the form of a set of equations based on known physics or correlations of input data to outputs. The parametric model is not practical in a number of instances where the knowledge embodied in the first principle is not known or not available. In these instances, a readily adaptable framework is required to assist process engineers in creating a process model without advance knowledge such as the first principle.